_P_5 _L___P- a
POLICY RESEARCH WORKING PAPER 2059
Financial Intermediation Legal and accounting reform
that strengthens creditor
and Growth rights, contract enforcement,
and accounting practices
Causality and Causes boosts financial development
and accelerates economic
growth.
Ross Levinie
Normani Loayza
Thorsten Beck
The World Bank
Development Research Group
NMacroeconomics and Growth
Februarv 1999
POLICY RESEARCH WORKING PAPER 2059
Summary findings
Levine, Loayza, and Beck evaluate techniques, they find that development of financial
* Whether the level of development of financial intermediaries exerts a large causal impact on growth.
intermediaries exerts a casual influence on economic The data also show that cross-country differences in
growth. legal and accounting systems help determine differences
* Whether cross-country differences in legal and in financial development.
accounting systems (such as creditor rights, contract Together, these findings suggest that legal and
enforcement, and accounting standards) explain accounting reform that strengthens creditor rights,
differences in the level of financial development. contract enforcement, and accounting practices boosts
Using both traditional cross-section, instrumental- financial development and accelerates economic growth.
variable procedures and recent dynamic panel
This paper - a product of Macroeconomics and Growth, Development Research Group - is part of a larger effort in the
group to understand the links between the financial system and economic growth. Copies of the paper are available free
from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Kari Labrie, room MC3-456, telephone
202-473-1001, fax 202-522-1155, Internet address klabrie@worldbank.org. Policy Research Working Papers are also
posted on the Web at http://www.worldbank.org/html/dec/Publications/Workpapers/home.html. Thorsten Beck may be
contacted at tbeck@worldbank.org. February 1999. (47 pages)
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about
development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The
papers carry the names of the authors and should he cited accordingly. The findings, interpretations, and conclusions expressed in this
paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the
countries they represent.
Produced by the Policy Research Dissemination Center
Financial Intermediation and Growth: Causality and Causes
Ross Levine, Norman Loayza, Thoirsten Beck
* Levine: University of Virginia; Loayza: World Bank; Beck: University of Virginia and World Bank.
We thank semninar participants at the University of Illinois, the Federal Reserve Bank of Richmond,
the University of Minnesota, and Jonathan Wright for helpful comments.
1
Introduction
Do better functioning financial intermediaries - financial intermediaries that are better at
ameliorating information asymmetries and facilitating transactions - exert a causal influence on
economic growth? If they do, what determines the level of financial intermediary development?
Providing evidence on causality will influence the degree of urgency attached to policy reforms
designed to promote financial intermediary development. Providing evidence on the causes of financial
intermediary development will help policymakers design reforms i:hat indeed promote growth-enhancing
financial sector development. Furthermore, evidence on causality will help resolve a long debate among
econornists. A rich literature emphasizes the causal role of intermediaries in spurring growth [Bagehot
1873; Schumpeter 1912]. For instance, Alexander Hamilton (178 1) argued that "banks were the
happiest engines that ever were invented" for creating economic growth. Others, however, question
whether finance exerts a first-order, causal impact on economic activity [Robinson 1952; Lucas 1988].
President John Adams (1819) even asserted that banks harm the "morality, tranquility, and even wealth"
of nations.
This paper rigorously addresses the issue of causality and also provides suggestive evidence
concerning the determinants of financial development. Specifically, we evaluate (1) whether the level of
financial intermediary development exerts a causal influence on economic growth and (2) whether
cross-country differences in particular legal and accounting system characteristics (e.g., the legal rights
of creditors, the efficiency of contract enforcement, and accounting standards) explain cross-country
differences in the level of financial intermediary development. While past work shows that the level of
financial development is a good predictor of economic growth, these results do not settle the issue of
2
causality.' Financial development may simply be a leading indicator, rather than an underlying cause of
economic growth. This paper uses new data and new econometric procedures to shed considerably
more light on the issue of causality while also illuminating a close empirical association between key
legal and accounting characteristics and financial intermediary development. In conducting this analysis,
we evaluate the predictions that emerge from an array of theoretical models.2
Methodologically, the paper uses two econometric techniques to study causality. First, we use a
pure cross-sectional estimator, where data for 71 countries are averaged over the period 1960-1995,
with one observation per country. As in much of the cross-country growth literature, the dependent
variable is the growth rate of the real per capita Gross Domestic Product (GDP). The regressors
include a variable of particular interest, in our case financial intermediary development, along with a set
of conditioning information. Unlike much of the literature, we use instrumental variables to extract the
exogenous component of financial intermediary development. Specifically, LaPorta, Lopez-de-Silanes,
Shleifer, and Vishny (1997, 1998; henceforth LLSV) note that most countries can be divided into
countries with predominantly English, French, German, or Scandinavian legal origins ancd that countries
typically obtained their legal systems through occupation or colonization. Thus, we view legal origin as
an exogenous "endowment." After extending the LLSV sample from 49 to 71 countries, we use the
legal origin indicators as instrumental variables to extract the exogenous component of financial
intermediary development.
' King and Levine (1993a,b) and Levine and Zervos (1998) show that measures of financial intermediary development
predict economic growth over the next 18 to 30 years in a broad-cross section of countries. Further, Neusser and Kugler
(1998) and Rousseau and Wachtel (1998) find that financial development Granger-causes economic periformance in time-
series studies of growth and financial development.
2 See, for instance, Diamond (1984), Boyd and Prescott (1986), Greenwood and Jovanovic (1990), Bencivenga and Smith
(1991), and King and Levine (1993b).
3
The second method for examining causality uses panel data and exploits the cross-country and
time-series dimensions of the data. We assemble a panel dataset, vith data averaged over each of the
seven 5-year intervals composing the period 1960-1995. We use two Generalized Method-of-
Moments (GM dynamic panel estimators that correct inherent problems with the purely cross-
sectional estimator. Specifically, these estimators address the econometric problems induced by
country-specific effects, endogeneity, and the routine use of lagged dependent variables in growth
regressions [Arellano and Bond 1991; Holtz-Eakin, Newey, and Rosen 1990]. In the first GMM panel
estimator, the strategy for addressing possible omitted variable bias created by country-specific effects is
to difference the regression equation. Thus, we first take differences to eliminate country-specific
effects and thereby remove omitted variable bias. Next, we instrurnent the right-hand-side variables (the
differenced values of the original regressors) using lagged values of the original regressors (measured in
levels) as instruments. This last step removes the inconsistency arising from simultaneity bias, including
biases induced by the differenced lagged dependent variable. This dfference dynamic-panel estimator
has increasingly been used in studies of growth. [Caselli, Esquivel, and LeFort 1996; Easterly, Loayza,
and Montiel 1997].
The second GMM dynamic panel estimator goes beyond the difference dynamic-panel estimator.
The problem with the difference estimator is that it generally suffer s from weak instruments, which
yields large biases in finite simples and poor precision even asymptotically [Alonso-Borrego and
Arellano 1996; Bond, Hoeffler, and Temple 1977]. Specifically, lagged values of the levels of the
original regressors frequently make weak instruments for the differenced values of the regressors used
in the dynamic-panel equation. This occurs in the current setting if lagged values of financial
development do not forecast changes in financial development. To mitigate this problem, we use a
4
system estimator. Besides the difference dynamic-panel equations where the instruments are lagged
levels of the original regressors, we simultaneously estimate the original, levels equation where the
instruments are lagged values of the differenced regressors [Arellano and Bover 1995]. By mitigating
the weak instruments problem, this system estimator offers dramatic improvements in both efficiency
and consistency in Monte Carlo simulations [Blundell and Bond 1997]. Thus, this paper uses two
procedures to address causality: a pure cross-sectional estimator and a GMM dynamic panel technique,
with both a difference and a system estimator.
While subject to some qualifications, the two methods produce very consistent findings
regarding causality: financial intermediary development exerts a large, causal irnpact on economic
growth. In conducting this research, we construct a new dataset and focus on three measures of
financial intermediation. One measures the overall size of the financial intermediation sector; the second
measures whether commercial banking institutions, or the central bank, is conducting the
intermediation, and the third measures the extent to which financial institutions funnel credit to private
sector activities. Our financial development indicators improve on past measures by (i) more accurately
deflating nominal measures of intermediary liabilities and assets, (ii) more comprehensively measuring
the banking sector, and (iii) more carefully distinguishing who is conducting the intermediation and to
where the funds are flowing. While the financial intermediary indicators are still imperfect measures of
how well financial intermediaries research firms, monitor managers, mobilize savings, pool risk, and
ease transactions, these three measures provide more inforrnation about financial intermediary
development than past measures and together they provide a more accurate picture than if we used only
a single measure. Moreover, they produce similar conclusions. Econometrically, the results indicate
that the close empirical association between finance and growth is not the result of simultaneity or
5
omitted variable bias. The exogenous component of financial intermediary development is positively
correlated with economic growth. Economically, the impact of finance on growth is large. For
example, the estimated coefficients suggest that if Argentina had enjoyed the level of financial
intermediary development of the average developing country during the 1960-95 period they would
have experienced about one percentage point faster real per capita GDP growth per annum over this
period.
Next, the paper searches for legal and accounting determinants of cross-country differences in
financial intermediary development. The data show that cross-country differences in legal systems and
accounting standards help determine cross-country differences in financial development. While every
legal/accounting indicator is not significantly correlated with every financial development indicator, the
legal/accounting indicators taken together are jointly significant at the 0.01 level with all of the financial
intermediary development measures. The data suggest that countries with legal and regulatory systems
that give a high priority to creditors receiving the full present value of their claims on corporations have
better functioning financial intermediaries than countries where the legal system provides weaker
support to creditors. Moreover, contract enforcement seems to matter even more than the formal legal
and regulatory codes. Countries that efficiently impose compliance with laws tend to have better
developed financial intermediaries than countries where enforcement is more lax. Finally, the paper
shows that information disclosure matters for financial developmernt. Countries where corporations
publish relatively comprehensive and accurate financial statements have better developed financial
intermediaries than countries where published information on corporations is less reliable. Taken
together, this paper's findings suggest that legal and regulatory changes that strengthen creditor rights,
6
contract enforcement, and accounting practices will boost financial intermediary development and
induce a rapid acceleration in economic growth.
This paper complements recent microeconomic efforts aimed at reconciling wheither financial
development is simply a good predictor of economic growth. Rajan and Zingales (1998) show that, in
countries with well-developed financial systems, industries that are naturally heavy users, of external
finance grow relatively faster than other industries. Alternatively, in countries with poorily developed
financial systems, industries that are naturally heavy users of external finance grow more slowly than
other industries. Using firm-level data, Demirguc-Kunt and Maksimovic (1996) show that firms in
countries with better developed financial systems grow faster than they could have grown without this
access. While these microeconomic studies must respectively identify (a) the "natural" tendency of
industries to use external funding and (b) how fast firms would have grown in different financial
environments, the results support the conclusion that better financial systems facilitate economic
development.3
The rest of the paper is organized as follows. Section II presents the results on causality, using
purely cross-sectional data, while Section III discusses and presents the differenced and system dynamic
panel results. Section IV provides information on how the legal and accounting environment explain
cross-country differences in financial development. Section V concludes.
3 Furthermore, this paper complements an innovative event study by Jayaratne and Strahan (1996). They show that when
individual states of the United States relaxed intrastate branching restrictions the quality of bank loans rose and per capita
GDP growth accelerated.
7
II. Finance and Growth: Causality Using Purely Cross-Sectional Anyses
To examine whether financial intermediary development exerts a causal impact on economic
growth, we need measures of financial intermediary development. We also need instrumental variables
to extract the exogenous component of financial development. This section discusses the measures of
financial intermediary development and then describes the indicators of national legal system origin,
which we use as instrumental variables in the purely cross-sectional ;malysis. Finally, the section
examines the causal relationship between financial intermediary development and growth.
A. Financial intermediary development
Numerous theoretical models show that economic agents may form financial intermediaries to
mitigate the costs of acquiring information and conducting transactions.4 More specifically, financial
intermediaries emerge to lower the costs of researching potential investments, exerting corporate
control, managing risk, and mobilizing savings. Theory further suggests that, by providing these
services to the economy, financial intermediaries influence savings and allocation decisions in ways that
may alter long-run growth rates.' Thus, modem economic theory provides an intellectual framework
for understanding how, ceteris paribus, countries with "better" financial intermediaries - financial
intermediaries that are better at acquiring information, exerting corporate control, managing risk, and
mobilizing savings - would grow faster than countries with less developed financial systems.
To evaluate the empirical predictions advanced by a variety of theoretical models regarding the
relationship between finance and growth, therefore, we would ideally like to construct measures of the
ability of different financial systems to research and identify profitable ventures, monitor and control
managers, ease risk management and facilitate resource mobilization. It is impossible, however, to
4 See Boyd and Prescott (1986), Diamond (1984) and the reviews by Gertler (1988) and Levine (1997).
For example, see Greenwood and Jovanovic (1990), Bencivenga and Smith (1991), and King and Levine (1993b).
8
construct accurate, comparable measures of these financial services for a broad cross-section of
countries over the past 35 years. Consequently, to measure the provision of financial services, this
paper constructs three indicators of financial intermediary development. While each has particular
strengths and weaknesses, we improve upon past measures of financial intermediary development.
LIQUID LIABILITIES equals liquid liabilities of the financial system (currency plus demand
and interest-bearing liabilities of banks and nonbank financial intermediaries) divided by GDP. This is a
typical measure of "financial depth" and thus of the overall size of the financial intermecdiary sector
[King and Levine 1993 a]. LIQUID LIABILITIES, however, does not consider the allocation of capital;
it is just an indicator of size. Thus, LIQUID LIABILITIES may not accurately reflect the provision of
financial services in an economy.
COMMERCIAL-CENTRAL BANK equals the ratio of commercial bank assets divided by
commercial bank plus central bank assets. COMMERCIAL-CENTRAL BANK measures the degree to
which the banks versus the central banks allocates society's savings. The intuition underlying this
measure is that banks are more likely to identify profitable investments, monitor managers, facilitate risk
management, and mobilize savings than central banks.
PRIVATE CREDIT equals the value of credits by financial intermediaries to the private sector
divided by GDP. This measure of financial development is more than a simple measure of financial
sector size. PRIVATE CREDIT isolates credit issued to the private sector, as opposed to credit issued
6One way this paper improves upon past measures of financial intermediary development is by accurately deflating
nominal measures of financial intermediary liabilities and assets. Specifically, while financial intermediary balance sheet
items are measured at the end of the year, GDP is measured over the year. Some authors try to correct for this problem by
using an average of financial intermediary balance sheet items in year t and t-l and dividing by GDP measured in year t
King and Levine 1993a]. This, however does not fully resolve the distortion, especially in highly inflationary
environments. This paper deflates end-of-year financial balance sheet items by end of year consumer price indices (CPI)
and deflates the GDP series by the annual CPI. Then, we compute the average of the real financial balance sheet item in
year t and t-1 and divide this average by real GDP measured in year t. This is described more fully in the data appendix.
9
to governments, government agencies, and public enterprises. Furl;hermore, it excludes credits issued
by the central bank. PRIVATE CREDIT is our preferred indicator because it improves on other
measures of financial development used in the literature. For example, King and Levine (1993a,b) use a
measure of gross claims on the private sector divided by GDP. But, this measure includes credits issued
by the monetary authority and government agencies, whereas PRIVATE CREDIT includes only credits
issued by banks and other financial intermediaries. Also, Levine and Zervos (1998) and Levine (1998)
use a measure of deposit money bank credits to the private sector diivided by GDP over the period
1976-1993. That measure, however, does not include credits to the private sector by non-deposit
money banks and it only covers the period 1976-1993. PRIVATE C(REDIT is a broader measure of
credit issuing financial intermediation and its time dimension is twice as long, 1960-1995. While
PRIVATE CREDIT does not directly measure the amelioration of information and transaction costs, we
interpret higher levels of PRIVATE CREDIT as indicating higher levels of financial services and
therefore greater financial intermediary development.
Table 1 provides summary statistics on the financial intermediary development indicators. The
values are computed as averaged over the period 1960-95. There is considerable variation across
countries. For example, PRIVATE CREDIT ranges from a low of 4 percent of GDP in Zaire to a high
of 141 in Switzerland.
B. Legal origin
Comparative legal scholars place countries into four major legal families, either English, French,
German, or Scandinavian, that descended from Roman law [Reynolds and Flores 1996]. As described
by Glendon et al. (1982), Roman law was compiled under the directjion of Byzantine Emperor Justinian
in the sixth century. Over subsequent centuries, the Glossators and Commentators interpreted, adapted,
10
and amended the Law [Berman 1997]. In the 17db and 18' centuries the Scandinavian countries
formalized their own legal codes. The Scandinavian legal systems have remained relatively unaffected
from the far reaching influences of the German and especially the French Civil Codes.
Napoleon directed the writing of the French Civil Code in 1804. He made it a priority to secure
the adoption of the Code in France and all conquered territories, including Italy, Poland, the low
countries, and the Habsburg Empire. Also, France extended her legal influence to parts of the Near
East, Northern and Sub-Saharan Africa, Indochina, Oceania, French Guyana, and the French Caribbean
islands during the colonial era. Furthermore, The French Civil Code was a major influence on the
Portuguese and Spanish legal systems, which helped spread the French legal tradition to Central and
South America.
The German Civil Code (Burgerliches Gesetzbuch) was completed almost a centuiy later in
1896. The German Code exerted a big influence on Austria and Switzerland, as well as China (and
hence Taiwan), Czechoslovakia, Greece, Hungary, Italy, and Yugoslavia. Also, the German Civil Code
heavily influenced the Japanese Civil Code, which helped spread the German legal tradition to Korea.
Unlike these Civil Law countries, the English legal system is common law, where the laws were
primarily formed by judges trying to resolve particular cases.
This paper takes national legal origin as an exogenous "endowment" since the EnglLish, French,
and German systems were spread primarily through conquest and imperialism. It is critical to recognize,
however, that exogeneity is not a sufficient condition for economically meaningful instrumnental
variables. It must also be the case that there are good reasons for believing that legal origin is closely
connected to factors that directly affect the behavior of financial intermediaries. Here, we rely on LLSV
(1998). They trace differences in legal origin through to differences in the legal rules covering secured
11
creditors, the efficiency of contract enforcement, and the quality of accounting standards. Thus, legal
origin is connected to legal and regulatory characteristics defining financial intermediary activities. We
discuss the relationship between legal origin and the legal and regulatory environment in more detail
below. Here, note that legal origin has a profound impact on financial intermediary development.
Table 2 presents regressions of the financial intermediary development indicators on the dummy
variables for English, French and German legal origin, relative to Scmndinavian origin (which is captured-
in the constant). We extend the LLSV (1998) data set from 44 countries (with financial intermediary
data) to 71. Some ofthe regressions also control for the level of real per capita GDP. The major
message is that countries with a German legal origin have better developed financial intermediaries.
While countries with a French legal tradition tend to have less well-developed institutions than other
countries on average, this result does not hold when controlling for the overall level of economic
development . In contrast, the dummy variable for a German legal tradition enters with a positive and
significant coefficient even after controlling for the level of real per capita GDP. Also, as indicated by
the P-values of the F-test that the explanatory variables do not explain the dependent variable, the legal
origin variables explain a significant fraction of the cross-country variation of the financial intermediary
development indicators.
C. Legal origin and growth in a pure cross-section of countries
1. Cross-sectional estimator
The pure cross-sectional analysis uses data averaged over 1960-95, such that there is one
observation per country. The basic regression takes the form:
GROWTH; = a + ,BFINANCEi + y' [CONDITIONING SET]; + Si,
12
where the dependent variable, GROWTH, equals real per capita GDP growth, FINANCE equals either
LIQUID LIABILITIES, COMMERCIAL-CENTRAL BANK, or PRIVATE CREDIT, and
CONDITIONING SET represents a vector of conditioning information that controls for ot]her factors
associated with economic growth.'
To examine whether cross-country variations in the exogenous component of financiial
intermediary development explain cross-country variations in the rate of economic growth, the legal
origin indicators are used as instrumental variables for FINANCE. Econometrically, given the vector of
instrumental variables, Z, and assuming that E[e]=O and that E[ss'1=2, where Q2 is unrestricted, this
implies a set of orthogonality conditions, E[Z'e]=O. This produces an instrumental variable estimator of
the coefficients in the cross-country growth equation. After computing these GMM estimates, the
standard Lagrange-Multiplier test of the overidentifying restrictions assesses whether the instrumental
variables are associated with growth beyond their ability to explain cross-country variation in banking
sector development.
2. Conditioning information set
To examine the sensitivity of the results, we experiment with different conditioning information
sets . We seek to reduce the chances that the cross-country growth regression either omits an important
variable or includes a select group of regressors that yields a favored result. We report the results with
three conditioning information sets. The simple conditioning information set includes the constant, the
logarithm of initial per capita GDP and initial level of educational attainment. The initial income
variable is used to capture the convergence effect and school attainment is used to control for the level
7Due to the potential nonlinear relationship between econoric growth and the assortment of economic indicators, we use
natural logarithms of the regressors.
13
of human capital. The policy conditioning information set includes the simple conditioning information
set plus measures of government size, inflation, the black market exchange rate premium, and openness
to international trade.' Thefill conditioning information set includes the policy conditioning
information set plus measures of political stability (the number of revolutions and coups and the number
of assassinations per thousand inhabitants (Banks 1994)) and ethnic diversity (Easterly and Levine
1997). Thus, for each of the three financial intermediary development indicators, we present regression
results for the (i) simple, (ii) policy, and (iii) full conditioning information sets.
3. Regression results
The results indicate a very strong connection between the exogenous component of financial
intermediary development and long-run economic growth. Table 3 summarizes the purely cross-
sectional instrumental variable results for nine regressions, where the instrumental variables are the legal
origin variables. For brevity, we report only the coefficients on the financial development indicators.
Each of the three financial intermediary development indicators (PRIVATE CREDIT, COMMERCIAL-
CENTRAL BANK, LIQUID LIABILITIES) is significantly correlated with economic growth at the
five percent significance level in the simple, policy, and full conditioning information set regressions.
The exogenous component of financial intermediary development is closely tied to long-run rates of per
capita GDP growth. Furthermore, the data do not reject the orthogonality conditions at the ten percent
level in any of the nine regressions. The inability to reject the orthogonality conditions plus the result
that the instruments are highly correlated with financial intermediary development (Table 2) suggest that
the instruments are appropriate. These results indicate that the stronig link between financial
development and growth is not due to simultaneity bias.
8 The black market exchange rate premium is frequently used as an overall index of trade, exchange rate, and price
distortions [Easterly 1994; Levine and Zervos 19931. The inflation rate and size of the government serve as indicators of
macroeconomic stability [Easterly and Rebelo 1993; Fischer 19931.
14
Besides suggesting that greater financial intermediary development causes faster economic
growth, the results indicate an economically large relationship. For example, India's value of
PRIVATE CREDIT over the 1960-95 period was 19.5 percent of GDP, while the mean value for
developing countries was 25 percent of GDP. The results suggest that an exogenous improvement in
PRIVATE CREDIT in India that had pushed it to the sample mean for developing countries would have
accelerated real per capita GDP growth by about 0.6 percentage point per year.9 Similarly, if Argentina
had moved from its value of PRIVATE CREDIT (16) to the developing country sample mean, it would
have grown more than one percentage point faster per year. This is large considering that growth only
averaged about 1.8 percent per year over this period.
D. Sensitivity Analyses
We have conducted a number of sensitivity analyses to gauge the robustness of these findings.
For instance, the two-stage least squares estimator gives similar results to the GMM estimator reported
above. We have also restricted the sample to those countries for which LLSV (1998) collect legal data.
This did not alter the results. Furthermore, as control variables, we used measures of the efficiency of
the bureaucracy and the level of bureaucratic red tape [Knack and Keefer 1995; Mauro 1995], as well
as measures of terms of trade changes and population growth. These did not alter our findlings. We
controlled for the level of stock market development (Levine and Zervos 1998) without chianging the
conclusion that financial intermediary development exerts a positive impact on long-run growth. We
also experimented with two additional measures of financial intermediary development. One measure
equals deposit money bank credit to the private sector divided by GDP. This is smaller then PRIVATE
CREDIT, which also includes other financial intermediaries. The second additional measure equals the
9 To get this, recall that the regressors are in logs and note that the ln(25) - ln(19.5) = 0.25. Then, use the smallest
parameter on PRIVATE CREDIT from Table 3, which equals 2.5, so that 2.5*(0.25) = 0.63.
15
ratio of deposit money bank domestic assets to GDP (and so does not distinguish between credits issued
to the private sector and those issued to the public sector). These two additional measures also suggest
that better financial intermediaries induce faster economic growth. Finally, we assess the sensitivity of
our findings to outliers, but we do not find any particular influential observations materially affecting the
coefficient on financial intermediation or its significance.
III. Finance and Growth: Causality Using Panel Procedures
After describing the underlying econometrics, this section presents results on causality using
dynamic panel estimators. Here, we use panel estimation procedlures. The panel consists of data for 74
countries over the period 1961-1995. We average data over non-overlapping, five-year periods, so that
data permitting there are seven observations per country (1961-65; 1966-70; 1971-75; etc.). Thus,
when we use the subscript "t" to designate a time-period, it represents one of these five-year averages.
A. GMM Estimators for Dynamic Panel Models
We use Generalized-Method-of-Moments dynamic panel estimators that control for unobserved
country-specific effects, the endogeneity of explanatory variables, time-specific effects, and the use
of lagged dependent variables.'0 Consider the following regression equation,
yt -yi,- = (a - l)y-,, + 6 Xi,, + Vi + t(1)
'° The Generalized Method of Moments (GMM) estimator was proposed by Chamberlain (1984), Holtz-Eakin, Newey
and Rosen (1988), Arellano and Bond (1991), and Arellano and Bover (1995), and has been applied to cross-country
studies by, among others, Caselli, Esquivel and Lefort (1996), Easterly, Loayza and Montiel (1997), and Fajnzylber,
Lederman, and Loayza (1998). For a concise presentation of the GMM estimator addressed to a general audience, see the
appendix of Easterly, Loayza, and Montiel (1997) and chapter 8 of Baltagi (1995).
16
where y represents the logarithm of real per capita GDP, X represents the set of explanatory
variables (other than lagged per capita GDP), 77 is an unobserved country-specific effect, e is the
error term, and the subscripts i'and t represent country and time period, respectively. Tl 1'he
dependent variable in equation (1) is the period's average growth'rate. We can rewrite eq[uation (1)
as a lagged-dependent variable equation as follows,
IYit = aYia-t + ,' Xi, + 77i + 0i t (2)
The usual method of dealing with the country-specific effect in the context of panel data has
been to first-difference the regression equation (Anderson and Hsiao 1981). In this way the specific-
effect is directly eliminated from the estimation process. First-differencing equation (2), awe obtain
Yi - = a(yita1i -yij-2) + ,6i (X, Xij ) + (-i - si t-) (3)
The use of instruments is again required to deal with two issues: first, the likely endogeneity
of the explanatory variables, X; and, second, the new error term, ei - ej,t-j is correlated with the
differenced lagged dependent variable, yj,t-l -Yi,t-2. This second issue arises by consiruction
when we difference equation (2).
We would like to relax the assumption that all the explanatory variables are strictly
exogenous (that is, that they are uncorrelated with the error term at all leads and lags). Relaxing this
We also include time dummies to account for time-specific effects.
17
assumption allows for the possibility of simultaneity and reverse causality, which are very likely
present in growth regressions. We adopt the assumption of weak exogeneity of the explanatory
variables, in the sense that they are assumed to be uncorrelated with future realizations of the error
term. This weaker assumption means that current explanatory variables may be affected by past and
current growth rates but not by future ones. In practice we assume that all variables are weakly
12
exogenous.
Under the assumptions that (a) the error term, e, is not serially correlated, and (b) the
explanatory variables, X, are wealdy exogenous, the following moment conditions apply to the
lagged dependent variable and the set of explanatory variables:
E[y; t ,3 (s,,- ,i)t-J = 0 for s > 2; t =3 ..., T (4)
E[Xi1t-8 * (e,ij - .t_1)] = 0 for s > 2 t=3, ...,T (5)
We use a consistent GMM estimator based on these moment conditions. We refer to this estimator
as the difference estimator.
There are, however, conceptual and statistical shortcomings with this estimator.
Conceptually, we would like to study not only the time-series relationship between financial
development and per capita GDP growth but also their cross-country relationship, which is
eliminated in the case of the simple difference estimator. Statistically, Alonso-Borrego and Arellano
(1996) and Blundell and Bond (1997) show that when the lagged dependent and the explanatory
'2Population growth rate and the growth rate of the terms of trade are assuned exogenous, but these variables are only
included in the regressions when we conduct sensitivity analyses.
18
variables are persistent over time, lagged levels of these variables are weak instruments for the
regression equation in differences. The instruments' weakness has repercussions on both the
asymptotic and small-sample performance of the difference estimator. As the variables' persistence
increases, the asymptotic variance of the coefficients obtained with the difference estimator rises
(that is, the asymptotic precision of this estimator deteriorates). Furthermore, Monte Carlo
experiments show that the weakness of the instruments produces biased coefficients in small
samples. This bias is exacerbated with the variables' over time persistence, the importance of the
specific-effect, and the smallness of the time-series dimension. An additional problem with the
simple difference estimator relates to measurement error: Differencing may exacerbate the bias due
to errors in variables by decreasing the signal-to-noise ratio (see Griliches and Hausman, 1986).
To confront these conceptual and statistical concerns, we use alternative estimators. Blundell
and Bond (1997) suggest the use of Arellano and Bover's (1995) system estimator that -- based on
asymptotic and small-sample properties -- reduces the potential biases and imprecision associated
with the usual difference estimator. Arellano and Bover (1995) present an estimator that combines,
in a system, the regression in differences with the regression in levels. The instruments for the
regression in differences are the same as above (i.e., the lagged levels of the corresponding,
variable), so that, the moment conditions in equations (4) and (5) apply to this first part of the
system. The instruments for the regression in levels are the lagged differences of the corresponding
variables. These are appropriate instruments under the following additional assumption: although
there may be correlation between the levels of the right-hand side variables and the country-specific
effect in equation (2), there is no correlation between the differences of these variables and the
country-specific effect. This assumption results from the following stationarity property,
19
+[i,t+p V7i ] = E[yi,,t+ - 7/i ](6)
and E[Xj,t+p * i]=E[Xi,t+q, i7I foralilpandq
Therefore, the additional moment conditions for the second part of the system (the regression in
levels) are given by the following equations:13
E[(Yi,,-- Yi,t-s-1) (7i +ei,t)] = 0 fors= 1 (7)
E[(Xi,t-s-Xi,t- s-) -(1i + ei,t)] = 0 fors= 1 (8)
Thus, we use the moment conditions presented in equations (4), (5), (7), and (8) and employ
a Generalized Method of Moments (GMM) procedure to generate consistent and efficient estimates
of the parameters of interest (Arellano and Bond, 1991; and Arellano and Bover, 1995).14
The consistency of the GMM estimator depends on whether lagged values of per capita GDP
and the other explanatory variables are valid instruments in the growth regression. To address this
issue we consider three specification tests suggested by Arellano and Bond (1991), Arellano and
13 Given that lagged levels are used as instruments in the differences specification, only the most recent difference is used
as instrument in the levels specification. Using other lagged differences would result in redundant moment conditions.
(see Arellano and Bover 1995)
4 We are grateful to Stephen Bond for providing us with a program to apply his and Arellano's estimator to an
unbalanced panel data set.
20
Bover (1995), and Blundell and Bond (1997). The first is a Sargan test of over-identifying
restrictions, which tests the overall validity of the instruments by analyzing the sample analog of the
moment conditions used in the estimation process. The second test examines the hypothesis that the
error term ejj is not serially correlated. In the both the difference regression and the system
difference-level regression we test whether the differenced error term is second-order serially
correlated (by construction, it is likely that this differenced error term be first-order serially
correlated even if the original error term is not). The third test is the difference Sargan statistic,
which tests the additional set of restrictions of the system estimator. This "difference" Sargan statistic
is asymptotically distributed as Chi-square under the null hypothesis of validity of the additional
instruments."5 Failure to reject the null hypotheses of these tests gives support to the model.
B. Results
The dynamic panel estimates suggest that financial intermediary development exerts a large,
positive causal impact on economic growth. Table 4 presents the results using the difference and
system estimators described above. We also present the results when the panel estimation is ]performed
purely in levels for comparative purposes. In Table 4, only the results on the financial indicators are
given. Table 5 gives the full results from system dynamic-panel estimation. The analysis was conducted
with two conditioning information sets. The first uses the simple conditioning information set, which
includes initial income and educational attainment. The second uses the policy conditioning information
set, and includes initial income, educational attainment, government size, openness to trade, inflation,
lS The degrees of freedom of the "difference" Sargan test is given by the number of additional restrictions in the system
estimator (which is given by the difference between the number of degrees of freedom of the system estimator and that of
the difference estimator).
21
and the black market exchange rate preniium. 16 Table 5 also presents (1) the Sargan test, where the null
hypothesis is that the instrumental variables are uncorrelated with the residuals and (2) the serial
correlation test, where the null hypothesis is that the errors in the differenced equation exhibit no
second-order serial correlation.
The three financial intermediary development indicators (LIQUID LIABILITIES,
COMMERCIAL-CENTRAL BANK, and PRIVATE CREDIT) are significant at the 0.05 significance
level in the levels, difference, and system dynamic panel growth regressions, with one exception. The
coefficient on LIQUID LIABILITIES is insignificant in the difference dynamic panel growth regression
with the policy conditioning information set. While this may indicate a somewhat less robust link when
using a purely "size" measure of financial intermediary development, LIQUID LIABILITIES enters the
levels and system dynamic panel growth regressions significantly in all specifications. Put differently,
after controlling for country-specific effects, endogeneity, and potential problems associated with lagged
dependent variables and weak instruments, the data suggest a strong, positive, link between financial
intermediary development and economic growth. Furthermore, there is no evidence of second order
serial correlation and the regressions pass the Sargan specification test. In the system dynamic panel
estimates, we do not reject the difference Sargan test; that is, we do not reject the assumption that the
country-specific effect is uncorrelated with the differences of the regressors. It is also worth noting that
many of the other regressors also enter significantly with the expected signs (Table 5).
The regression estimates are also economically large. As shown the coefficients that emerge
from the dynamic panel estimation are very close to those that we obtain from the purely cross-section,
instrumental-variable estimation. For example, PRIVATE CREI)IT has a coefficient of 2.5 in the cross-
16 We do not use the full conditioning information set with data on political and institutional variables in the panel
estimates. These variables frequently have very limited, if any, time-dimension.
22
section results (the simple conditioning information set regression in Table 3), while PRIVATE
CREDIT has a coefficient of 2.24 in the system dynamic-panel results reported in Table 4.17 As noted
earlier, these coefficients suggest that exogenous changes in financial intermediary development imply
large changes in economic growth.
C. Sensitivity Analyses and Discussion
The dynamic panel results are also robust to a variety of sensitivity analyses. For instance, when
we use alternative measures of financial intermediary development (deposit money bank credit to the
private sector divided by GDP; and the ratio of total deposit money bank domestic assets to GDP), we
still find a strong, causal relationship between finance and growth. Furthermore, if we control for terms
of trade changes and population growth, we obtain virtually identical results to those reported in Tables
4 and 5*is Finally, when we include the legal origin variables as instruments in the dynamic panel
estimates, we still find that financial intermediary development exerts a large, causal impact on
economic growth.
" There is some divergence in coefficient estimates between the system dynamic panel estimates, Table 4, and the cross-
sectional IV regressions, Table 3, when the conditioning information set is expanded to include the policy conditioning
information set. The cross-section regression produces a coefficient estimate of 3.2 on PRIVATE CREDIT, while the
panel yields a coefficient of 1.4. Besides exploiting the time-series dimension of the data, the dynamic-panel also
recognizes the endogeneity of the other regressors, which may help account for the different coefficient estimates.
18 Note, that in the system dynamic panel regressions with the policy conditioning information set, the number of
instrumental variables is larger than the number of cross-sectional observations (i.e., countries). This "over-fitting" of
the data can bias the t-statistics upwards. This arises when the variance-covariance matrix is constructed from the first-
stage residuals in order to allow for non-spherical distributions of the error term - and thereby get more efficient estimates
in the second stage. (However, this "over-fitting" problem does not plague (a) the simple conditioning information set
regressions, or (b) the level or difference estimators because there are many more countries than instruments in these
specifications.) More generally, the two-step GMM estimator sometimes converges to its asymptotic distribution only
slowly. Thus, we also considered the first-stage results, which assume homoskedasticity and independence of the error
terms. These first-stage results also indicate that financial intermediary development exerts a causal impact on economic
growth.
23
IV. Searching for Determinants of Financial Intermediary Development
The last two sections presented evidence consistent vwith the view that financial intermediation
exerts a positive, economically large impact on long-run economic growth. This section undertakes a
linmited -- more speculative -- search of potential legal and accounting determinants of financial
intermediary development. '9 This exploratory search shows that newly available information on
particular features of national legal systems and accounting standards are closely associated with cross-
country differences in financial intermediary development. AEter describing the empirical measures of
the legal and accounting environment, we study the connection between the legal environment and
financial intermediary development and then trace this link through to long-run growth.
A. The legal and accounting environment
This paper uses three indicators to characterize differences in national legal and regulatory
systems: the legal rights of creditors, the soundness of contract enforcement, and the level of corporate
accounting standards.20
1. Creditor rights
The ability of financial intermediaries to persuade firms to pay their loans differs across national
legal systems. Legal systems differ in terms of the rights of financial institutions to repossess collateral
or liquidate firms in the case of default. Legal systems vary in terms of the rights of creditors to remove
19 Also, note that evidence on the determinants of financial intermediary development is informative regardless of
causality. Financial intermediaries may affect business-cycles [Bernanke and Gertler 1989,1990; Williamson 1987] and
North (1981) notes that understanding the evolution of key institutions, such as financial institutions, is critical for
understanding the complex process of economic development.
20 The legal data are available for 44 countries in our sample. In section II, we did not use these as instrumental variables
because legal codes, enforcement quality, and accounting standards may be influenced by economic development. Thus,
we feel comfortable arguing - and specification tests did not reject this argument - that legal origin is an exogenous
endowment. We feel a bit less comfortable arguing that economic growth does not affect accounting standards.
Nonetheless, we employ these legal data as instruments below to show that particular features of the legal environment are
strongly linked with both financial sector performance and long-run grovvth.
24
managers in corporate reorganizations. Finally, legal systems differ in terms of the priority given to
secured creditors relative to other claimants in corporate bankruptcy.
More specifically, this paper uses four measures of the legal rights of banks.
AUTOSTAY equals one if a country's laws impose an automatic stay on the assets of firms
upon filing a reorganization petition. AUTOSTAY equals 0 if this restriction does not appear in the
nation's legal codes. The restriction would prevent creditors from gaining possession of collateral or
liquidating a firm to meet a loan obligation. Thus, all else equal, AUTOSTAY should be negatively
correlated with the activities of credit issuing intermediaries.
MANAGES equal one if firm managers continue to administer the firm's affairs pending the
resolution of reorganization processes, and zero otherwise. In some countries, management stays in
place until a final decision is made about the resolution of claims. In other countries, manage:ment is
replaced by a team selected by the creditors. If management stays pending resolution, this reduces
pressure on management to pay creditors. Thus, MANAGES should be negatively correlated with the
activities of credit issuing intermediaries. Here it is important to highlight a substantive wealness with
AUTOSTAY and MANAGES. They do not measure the efficiency of the legal and regulatory system
in coping with bankruptcy. For instance, two countries could have very similar legal codes, such that
management stays in place pending the resolution of a bankruptcy hearing and there is an automatic stay
on the assets of a firm until the reorganization petition is processed by the bankruptcy courts. However,
the two countries legal and regulatory systems may process bankruptcy and reorganization very
differently. One country's system may take a long-time and be subject to great uncertainty. T'he other
may be very rapid, efficient, and transparent. Thus, a major difference across countries may be the
25
quality of the bankruptcy system, not the laws themselves. Currently, there do not exist cross-country
measures of the speed, transparency, and fairness of bankruptcy systems.
The third measure of the legal rights of credits is SECURED 1, which equals one if secured
creditors are ranked first in the distribution of the proceeds that result from the disposition of the assets
of a bankrupt firm. SECURED 1 equals zero if non-secured creditors, such as the government or
workers get paid before secured creditors. In cases where SEC(URED1 equals zero, this certainly
reduces the attractiveness of lending secured credit. SECURED 1 should be positively correlated with
activities of intermediaries engaged in secured transactions, holding everything else constant.
CREDITOR is a cumulative index of these creditor rights indicators and equals CREDITOR =
SECURED 1 - AUTOSTAY - MANAGES. CREDITOR takes on values between 1 (best) and -2
(worst).2' One would expect countries with higher values of C(REDITOR to have stronger creditor
rights and better-developed financial intermediaries, all else equal.
Table 6 gives summary statistics on CREDITOR. As shown there is substantial cross-country
variation in CREDITOR, where the maximum value is 1, the minimum value is -2, and the standard
deviation is about 1. Brazil, Colombia, France, Mexico, Peru, and the Philippines (all countries with a
French legal origin) are countries where CREDITOR=-2, indicating that their legal systems do not
stress the rights of creditors. In contrast, the legal codes of Egypt, Hong Kong, India, Indonesia, Israel,
Korea, Malaysia, Nigeria, Pakistan, Singapore, Thailand, United Kingdom, and Zimbabwe stress the
rights of creditors, such that CREDITOR= 1. CREDITOR is an indicator of legal codes, however, it
does not incorporate information regarding enforcement.
2. Enforcement
21 We could have redefined AUTOSTAY and MANAGES such that values of one indicated stronger (instead of weaker)
creditor rights. This would have produced values of CREDITOR between 0D and 3 and would not have altered the results.
We did not do this for consistency: the variables in this paper are defined the same as the variables in LLSV (1997,1998).
26
The laws governing secured creditors will affect secured creditors only to the extent that the
laws are enforced. Consequently, measures of the efficiency of the legal system in enforcing contracts
are included from LLSV (1998).
RULELAW is an assessment of the law and order tradition of the country that ranges from 10,
strong law and order tradition, to 1, weak law and order tradition. This measure was constructed by
International Country Risk Guide (ICRG) and is an average over the period 1982-1995. Given the
contractual nature of banking, higher values of the RULELAW are likely to positively influence
banking development.
CONRISK is an assessment of the risk that a government will - and therefore can - modify a
contract after it has been signed. CONRISK ranges from 10, low risk of contract modification, to 1,
high risk of contract modification. Specifically, "modification" means either repudiation, postponement,
or reducing the government's financial obligation. This measure was constructed by ICRG and is an
average over the period 1982-1995. Legal systems that effectively enforce contracts will tend to
support banking activities.
ENFORCE equals the average of RULELAW and CONRISK. The empirical analyses focus on
this aggregate index of the efficiency of the legal system in enforcing contracts, ENFORCE., and the
aggregate index of creditor rights, CREDITOR.
Summary statistics on ENFORCE are given in Table 6. As shown, there is substantial cross-
country variation in ENFORCE, where the maximum value is 9.99, the minimum value is 3.55, and the
standard deviation is 2.2. The countries with very high values of enforcement, values of ENFORCE
greater that 9, are Australia, Austria, Belgium, Canada, Denmark, Finland, France, German, Japan,
Netherlands, New Zealand, Norway, Sweden, and Switzerland. In contrast, countries where contract
27
enforcement is poor, values of ENFORCE less than 5, include Colombia, Nigeria, Pakistan, Philippines,
Peru, and Zimbabwe.
3. Accounting standards
Information about corporations is critical for exerting corporate governance and
identifying the best investments. Accounting standards that simplify the interpretability and
comparability of information across corporations will simplify financial contracting. Furthermore,
financial contracts that use accounting measures to trigger paiticular actions can only be enforced if
accounting measures are sufficiently clear. Accounting standards differ across countries and
governments impose an assortment of regulations regarding inrFormation disclosure. Since accurate
information about corporations may improve financial contracting and intermediation, the paper
examines a measure of the quality of information disclosed through corporate accounts from LLSV
(1998).
ACCOUNT is an index of the comprehensiveness of company reports. The maximum possible
value is 90 and the minimum is 0. The Center for International Financial Analysis and Research
assessed general accounting information, income statements, balance sheets, funds flow statement,
accounting standards, and stock data in company reports in 1990. We expect ACCOUNT to be
positively correlated with financial intermediary development." As shown in Table 6, ACCOUNT
exhibits substantial cross-country variation. The maximum value is 83, Sweden, while the minimum
22 This is not necessarily true and raises the need for a general conceptual qualification. An economy with perfect
information, perfect contract enforcement and perfect legal codes (i.e., and economy with essentially zero transaction and
information costs) would have little reason for financial intermediaries. PFut differently, market frictions motivate the
emergence of financial intermediaries, e.g., Boyd and Prescott (1986). Conceptually, this implies that at vely high levels
of legal system development and information dissemination, a marginal increase in legal efficiency or information quality
may cause a reduction in the role and importance of financial intermediaries. However, quadratic expressions for
ACCOUNT and CONRISK never entered significantly.
28
value in our sample is Egypt (24). The United States has a value of 71, which is well above the mean
value of 61.
B. Determinants of Financial Intermediary Development and Growth
This section examines the links between the legal environment and financial intermediiary
development and traces this link through to long-run growth. First, note that the legal origin variables
help explain cross country differences in creditor rights, enforcement quality, and accounting standards.
As shown by LLSV (1998), English legal tradition countries have laws that emphasize the rights of
creditors to a greater degree than the French, German, and Scandinavian countries. French civil law
countries protect creditors the least, with German and Scandinavian civil law countries falling) in the
middle. In terms of enforcement quality, countries with a French legal heritage have the lowest quality
of law enforcement, while countries with German and Scandinavian legal traditions tend to be the best
at enforcing contracts. Finally, LLSV (1998) show that countries with an English legal tradiition tend
to have much better accounting standards than French or German civil law countries.
Table 7 shows that cross-country differences in creditor rights, enforcement quality, and
accounting standards help explain cross-country differences in financial intermediary development, even
after controlling for the level of income per capita. Jointly, the variables CREDITOR, ENFORCE, and
ACCOUNT explain a significant amount of the cross-country variation in the three financial
intermediary indicators (PRIVATE CREDIT, LIQUID LIABILITIES, and COMMERCIAL-
CENTRAL BANK). Each of the legal/accounting indicators, however, is not significantly correlated
with all of the intermediary measures. For instance LIQUIID LIABILITIES is most closely associated
with ENFORCE and CREDITOR. In turn, COMMERCIAL-CENTRAL BANK and PRIVATE
CREDIT are very strongly linked with ENFORCE and ACCOUNT. The basic message that emerges
29
from Table 7 is that countries with (i) laws that give a high piriority to secured creditors, (ii) legal
systems that rigorously enforce contracts, and (iii) accounting standards that produce comprehensive
and comparable corporate financial statements tend to have better developed financial intermediaries.
Furthermore, Table 8 shows that creditor rights, enforcement quality, and accounting standards
influence financial intermediary development, and that this component of financial intermediary
development positively affects economic growth. Specifically, Table 8 uses CREDITOR, ENFORCE,
and ACCOUNT as instrumental variables within the context of the pure cross-country growth
regression framework described in Section II above. As shown, the data indicate that the component of
financial intermediary development defined by particular characteristics of the legal and accounting
environment is positively associated with economic growth. This strong link is robust to changes in the
conditioning information set. While there are good reasons to believe that contract enforcement and
accounting standards may be influenced by economic growth, these instruments pass the LM
specification test. Thus, particular characteristics of the legal and regulatory regime seem to influence
economic growth by affecting quality of financial intermediation.
B. Discussion of Causes
While still speculative, these findings (in conjunction with those in LLSV 1998) are consistent
with the view that countries with particular legal origins tend to create particular types of laws,
regulations, and enforcement mechanisms. It is these laws, regulations, and enforcement mechanisms
that help determine the level of financial intermediary development and thus long-run economic growth.
Put differently, when countries are endowed with a certain legal heritage, one might view them as being
endowed with a probability distribution regarding the laws, regulations, and enforcement mechanisms
associated with financial activities. Thus, for example, the data suggest that countries with a French
30
Civil Code have a lower probability of selecting laws that give a higher priority to secured credits,
selecting accounting standards that produce high-quality corporate financial statements, and rigorously
enforce contracts than countries with English, German, and Scandinavian legal systems. The resultant
laws, regulations, and enforcement mechanisms then affect the ability of the financial system to research
firms, exert corporate control, mobilize savings, and provide risk management and transactions services.
While it is difficult to change legal origin, the results offer a strategy for boosting financial development
and accelerating long-run growth. Countries can target reforms that ensure that lenders have
confidence that the legal system will quickly, transparently, and effectively enforce their claims against
borrowers and that outside investors have easy access to high-quality, comprehensive, and comparable
information about firms.
V. Conclusions
This paper first examined the issue of causality: Does greater financial intermediary development
cause faster economic growth? We use two econometric approaches. We use a pure cross-sectional,
instrumental variable estimator with one observation per country. The data are averaged over the
period 1960-1995. We also create a panel-data set, where we average the data over each (non-
overlapping) five-year period. Thus, data permitting, there are seven observations per country over the
1960-95 period. With this panel data set, we use a difference dynamic panel estimator developed by
Arellano and Bond (1991) and Holtz-Eakin, Newey, and Rosen (1988) and a system dynamic panel
estimator developed and studied by Arellano and Bover (1995) and Blundell and Bond (1997) that
mitigates some of the biases frequently found when using the difference dynamic panel estimator. Both
31
cross-section and panel-data results tell the same story: financial intermediary development exerts a
statistically significant and economically large impact on economic growth.
Next, we investigate whether cross-country differences in the legal rights of creditor, the
efficiency of contract enforcement, and accounting system stEmdards help explain cross country
differences in the level of financial intermediary development. The results are clear: Countries with (1)
laws that give a high priority to secured creditors getting the fiull present value of their claims against
firms, (2) legal systems that rigorously enforce contracts, including government contracts, and (3)
accounting standards that produce high-quality, comprehensive and comparable corporate financial
statements tend to have better developed financial intermediaries. The paper's findings are consistent
with the view that legal and accounting reforms that strengthen creditor rights, contract enforcement,
and accounting practices can boost financial intermediary development and thereby accelerate economic
growth. Due to data limitations, however, we do not conduct. a comprehensive evaluation of the
regulatory determinants of financial intermediary development [e.g., see Calomiris 1989; Kane
1985,1989; Barth, Nolle, and Rice 1996; BIS 1997; Calomiris and Gorton 1991; Kroszner and Rajan
1994; and Kroszner and Strahan 1996]. Future work would substantially broaden and deepen our
understanding of the determinants of financial intermediary development by obtaining additional
measures of the legal, supervisory, and regulatory factors that determine the level of financial
intermediary development.
32
REFERENCES
Adams, John (1819) quoted from Hammond (1991, p. 36).
Alonso-Borrego, C. and Arellano, Manuel. "Symmnetrically Normalised Instrumental Variable
Estimation Using Panel Data," CEMFI Working Paper No. 9612, September 1996.
Arellano, Manuel and Bond, Stephen. "Some Tests of Specification for Panel Data: Monte Carlo
Evidence and an Application to Employment Equations," Review of Economic Studies 1991,
58, pp. 277-297.
Arellano, Manuel, and Bover, Olympia. "Another look at the Instrumental-Variable Estimation of Error-
Components Models," Journal of Econometrics 1995, 68, pp. 29-52.
Bagehot, Walter. Lombard Street. Homewood, IL: Richard D. Irwin, 1873 (1962 Edition).
Banks, Arthur S. "Cross-National Time Series Data Archive," Binghampton, NY: Center for Social
Analysis, State University of New York at Binghampton, 1994.
Barro, Robert J and Lee, Jong-Wha: "International Measures of Schooling Years and Schooling
Quality", AER Papers and Proceedings 1996, 86, pp. 218-223.
Barth, James R.; Nolle, Daniel E.; and Rice, Tara N. "Commercial Banking Structure, Regulation, and
Performance: An International Comparison," Economic and Policy Analysis Working Paper,
Office of the Comptroller of the Currency, United States. September 1996.
Bencivenga, Valerie R., and Smith, Bruce D. "Financial Internediation and Endogenous Growth,"
Review of Economics Studies, April 1991, 58(2), pp. 195-209.
Berman, Harold J. Law and Revolution: The Formation of the Western Legal Tradition. Cambridge,
MA: Harvard University Press, 1997.
Bernanke, Ben and Gertler, Mark. "Agency Costs, Net Worth, and Business Fluctuations," American
Economic Review, March 1989, 79(1), pp. 14-38.
Bernanke, Ben and Gertler, Mark. "Financial Fragility and Economic Performance," Quarterlv Journal
of Economics, February 1990, 105(1), pp. 87-114.
BIS. "Core Principles for Effective Banking Supervision," No. 30. Basle Committee on Bank
Supervision, 1997.
Blundell, Richard and Bond, Stephen. "Initial Conditions and Moment Restrictions in Dynarric Panel
Data Models," University College London Discussion Paper 97-07, 1997.
Bond, Stephen; Hoeffler, Anke; and Temple, Jonathan. "Panel Data Estimators and Cross-Country
Growth Regressions: A Cautionary Tale," Hertford College, Oxford mimeo, 1997.
33
Boyd, John H. and Prescott, Edward C. "Financial Intermediiary Coalitions," Journal of Economic
Theory, April 1986, 38(2), pp. 211-232.
Calomiris, Charles W. "Deposit Insurance: Lessons from the Record," Economic Perspectives, Federal
Reserve Bank of Chicago, May/June 1989.
Calomiris, Charles W. "The Costs of Rejecting Universal Banking: American Finance in the German
Mirror, 1870-1914," in Coordination and Informatign: Historical Perspectives on the
Organization of Enterprise. Eds.: Naomi R. Lamoreaux and Daniel M.G. Raff, Chicago:
National Bureau of Economic Research, University of Chicago, 1995, pp. 257-321.
Calomiris, Charles W. and Gorton, Gary. "The Origins of Banking Panics: Models, Facts, and Bank
Regulation," in Financial Markets and Financial Crise, Ed.: R. Glenn Hubbard, Chicago:
University of Chicago Press, 1991, pp. 109-174.
Caselli, Francesco; Esquivel, Gerardo; and LeFort, Fernando. "Reopening the Convergence Debate: A
New Look at Cross-Country Growth Empirics," Journal of Economic Growth, September 1996,
1, pp. 363-389.
Chamberlain, G. "Panel Data", in: Handbook of Econometrics Vol.2, Eds.: Z. Griliches and M.D.
Intriligator, Elsevier, Amsterdam, 1984, pp. 1247-1313.
Dernirguwc-Kunt, Asli and Maksimovic, Vojislav. "Financial C'onstraints, Uses of Funds, and Firm
Growth: An International Comparison," World Bank mimeo, 1996.
Demirgu9 -Kunt, Asli and Detragiache, Enrica. "The Determinants of Banling Crises: Evidence from
Developed and Developing Countries. World Bank Policy Research Group Working Paper, No.
1828, 1997.
Diamond, Douglas W. "Financial Intermediation and Delegated Monitoring," Review of Economic
Studies, July 1984, 51(3), pp. 393-414.
Easterly, William, and Rebelo, Sergio. "Fiscal Policy and Economic Growth: an Empirical
Investigation," Journal of Monetary Economics December 1993, 32(3), pp. 417-57.
Easterly, William and Levine, Ross. "Africa's Growth Tragecdy: Policies and Ethnic Divisions,"
Quarterly Journal of Economics, November 1997, IC17, pp. 1203-50.
Easterly, William; Loayza, Nornan; and Montiel, Peter. "Has; Latin America's Post Reform Growth
Been Disappointing?" Journal of International Economics. November 1997, 43(3/4), pp. 287-
312.
Engerman, Stanley L. and Sokoloff, Kenneth L. "Factor Endowments, Institutions, and Differential
Paths of Growth Among New World Economies: A View from Economic Historians of the
34
United States," in How Latin America Fell Behind, Ed.: Stephen Haber, Stanford, CA: Stanford
University Press, 1996, pp. 260-304.
Faijnzylber, Pablo; Lederman, Daniel; and Loayza, Norman "What Causes Violent Crime," nmimeo,
March 1998, The World Bank.
Fischer, Stanley "The Role of Macroeconomic Factors in Growth," Journal of Monetary Economics,
December 1993, 32(3), pp. 485-511.
Gastil, R.D. "The Comparison Survey of Freedom: Experiences and Suggestions," Studies in!
Comparative International Development. 1990, 25, pp.25-50.
Gertler, Mark. "Financial Structure and Aggregate Economic Activity: An Overview," Journal of
Money. Credit, and Banking, August 1988, 20(3, Pt. 2), pp. 559-88.
Glendon, Mary Ann; Gordon, Michael; and Osakwe, Christopher. Comparative Legal Tradition in a
Nutshell. St. Paul: West Publishing Co., 1982.
Goldsmith, Raymond W. Financial Structure and Development. New Haven, CT: Yale University Press,
1969.
Greenwood, Jeremy and Jovanovic, Boyan. "Financial Development, Growth, and the Distribution of
Income," Journal of Political Economy, October 1990, 98(5, Pt. 1), pp. 1076-1107.
Greenwood, Jeremy and Smith, Bruce. "Financial Markets in Development, and the Developiment of
Financial Markets," Journal of Economic Dynamics and Control, January 1997, 21(1), pp. 145-
186.
Hamilton, Alexander (1781), quoted from Hammond (1991, p. 36)
Hammond, Bray. Banks and Politics in America: From the Revolution to the Civil War. Princeton, NJ:
Princeton University Press, 1991.
Hansson, Pontus and Jonung, Lars "Finance and Growth: The Case of Sweden 1834-1991," mimeo,
Sweden: Stockholm School of Economics.
Hicks, John. A Theory of Economic History. Oxford: Clarendon Press, 1969.
Holtz-Eakin, D.; Newey, W, and Rosen, H. "Estimating Vector Autoregressions with Panel Data,"
Econometrica, 1990, 56(6 , pp. 1371-1395.
Jayaratne, Jith and Strahan, Philip E. "The Finance-Growth Nexus: Evidence from Bank Branch
Deregulation," Quarterly Joumal of Economics, August 1996, 111(3), pp. 639-670.
Kane, Edward J. "How Incentive-Incompatible Deposit-Insurance Funds Fail," National Bureau of
Economic Research Working Paper, No. 2836, 1989.
35
Kane, Edward J. The Gathering Crisis in Federal Deposit Insurance, Cambridge: MIT Press, 1985.
King, Robert G. and Levine, Ross. "Finance and Growth: Schuimpeter Might Be Right," Quarterlv
Journal of Economics, August 1993a, 108(3), pp. 717-38.
King, Robert G. and Levine, Ross. "Finance, Entrepreneurship, and Growth: Theory and Evidence,"
Joumal of Monetary Economics, December 1993b, 32(3), pp. 513-42.
Knack, Stephen and Philip Keefer, "Institutions and Economic ]?erformance: Cross-Country Tests
Using Alternative Institutional Measures," Economic and Politics, 1995, 7, pp. 207-27.
Kroszner, Randall S. and Rajan, Raghuram G. "Is the Glass-Steagall Act Justified? A Study of the US
Experience with Universal Banking Before 1933, American Economic Review, September 1994,
84(4), pp. 810-832.
Kroszner, Randall S. and Strahan, Philip E. "Regulatory Incentives and the Thrift Crisis: Dividends,
Mutual-to-Stock Conversions, and Financial Distress," J,ournal of Finance, September 1996,
51(3), pp. 1285-1320.
Laporta, Rafael; Lopez-de-Silanes, Florencio; Shleifer, Andrei; and Vishny, Robert W. "Law and
Finance," Journal of Political Economv forthcoming 1998.
Laporta, Rafael; Lopez-de-Silanes, Florencio; Shleifer, Andrei; and Vishny, Robert W. "Legal
Determinants of External Finance," Journal of Finance, July 1997, 52(3), pp. 1131-1150.
Levine, Ross. "Financial Development and Economic Growth: Views and Agenda," Journal of
Economic Literature, June 1997, 35(2), pp. 688-726.
Levine, Ross. "The Legal Environment, Banks, and Long-Run Economic Growth," Journal of Money.
Credit. and Banking, August 1998,forthcoming.
Levine, Ross and Renelt, David. "A Sensitivity Analysis of Cross-Country Growth Regressions,"
American Econornic Review, September 1992, 82(4), pp. 942-63.
Levine, Ross and Zervos, Sara. "Stock Markets, Banks, and Economic Growth," American Economic
Review, June 1998, forthcoming.
Loayza, Norman; Lopez, Humberto; Schmidt-Hebbel, Klaus and Serven, Luis "World Saving
Database", mimeo 1997, The World Bank.
Mauro, Paolo. "Corruption and Economic Growth," Quarterlv Journal of Economics, August 1995,
110(3), pp. 681-712.
McKinnon, Ronald I. Money and Capital in Economic Development. Washington, D.C.: Brookings
36
Institution, 1973.
Merton, Robert C. "Financial Innovation and Economic Performance," Journal of Applied Corporate
Finance, Winter 1992, 4(4), pp. 12-22.
Neusser, Klaus and Kugler, Maurice. "Manufacturing Growth and Financial Development: Evidence
from OECD Countries," Review of Economics and Statistics, forthcoming 1998.
North, Douglas C. Structure and Change in Economic History, New York: Norton, 1981.
Patrick, Hugh. "Financial Development and Economic Growth in Underdeveloped Countries,"
Economic Development Cultural Change, January 1966, 14(2), pp. 174-89.
Rajan, Raghuram G. and Zingales, Luigi. "Financial Dependence and Growth," American ESconomic
Review, June 1998.
Reynolds, Thomas H. and Flores, Arturo A. Foreign Law Current Sources of Codes and Legislation in
Jurisdictions of the World, Fred B. Rothman & Co. (Littleton Colorado), (AALL Publication
Series No. 33). 1996.
Robinson, Joan. "The Generalization of the General Theory," in The Rate of Interest and Other Essays,
London: MacMillan, 1952.
Rousseau, Peter L. and Wachtel, Paul. "Financial Intermediation and Economic Performance: Historical
Evidence from Five Industrial Countries," Journal of Money. Credit, and Banking,forthcoming,
1998.
Schumpeter, Joseph A. The Theory of Economic Development, 1912, translated by Redvers Opie.
Cambridge, MA: Harvard University Press, 1934.
Shleifer, Andrei and Vishny, Robert W. "A Survey of Corporate Governance," Journal of Finance, June
1997, 52(2), pp. 737-84.
Shleifer, Andrei and Vishny, Robert W. "Corruption," Quarterlv Journal of Economics, 109, 1993, pp.
599-617.
Williamson, Stephen D. "Financial Intennediation, Business Failures, and Real Business Cycles,"
Journal of Political Economy, December 1987, 95(6), pp. 135-45.
37
Data ADgendix
A. Countries in the Sample
*-Not in the 71 country pure cross-sectional data set.
* *-Not in the 74 country panel data set.
Algeria* Greece Panama
Argentina Guatemala Papua New Guinea
Australia Guyana** Paraguay
Austria Haiti Peru
Bangladesh** Honduras Philippines
Barbados** Iceland** Portugal
Belgium India Rwanda*
Bolivia Indonesia* Senegal
Brazil Iran* Sierra Leone
Cameroon* Ireland South Africa
Canada Israel Spain
Central African Republic* Italy Sri Lanka
Chile Jamaica Sudan*
Colombia Japan Sweden
Costa Rica Kenya Switzerland
Cyprus Korea Syria
Denmark Lesotho* Taiwan**
Dominican Republic Liberia** Thailand
Ecuador Malawi* Togo
Egypt* Malaysia Trinidad and Tobago
El Salvador Malta** United States of America
Fiji** Mauritius Uruguay
Finland Mexico Venezuela
France Nepal** Zaire
Gambia* Netherlands Zimbabwe
Germany New Zealand
Ghana Nicaragua*
Great Britain Niger
Norway
Pakistan
B. Data Sources
1. Log level and growth rate of per capita GDP, from Loayza et al. (1997).
2. Government size is government expenditures as share of GDP, from Loayza et al. (1997).
3. Openness to trade is the sum of exports and imports as share of GDP, from Loayza et al. (1997).
4. Inflation rates are calculated using CPI data from the International Financial Statistics (IFS), line 64.
5. The average years of secondary schooling in the total population (15 years and over) come from
Barro and Lee (1996):
6. Data on the black market premium are from World's Currency Yearbook; and Adrian Wood,
Global trends in real exchange rates: 1960-84, WB Discussion paper no. 35. 1988.
7. Data on Liquid Liabilities are calculated using IFS numbers, using the following method:
{(0.5)*[F(t)/P_e(t) + F(t-l)/P_e(t-l)] }/[GDP(t)/P_a(t)]
where F is liquid liabilities (line 551), GDP is line 99b, P_e is end-of period CPI (line 64) and P_a is the
average annual CPI.
8. Data on Commercial versus Central Bank are calculated using IFS numbers, using the following
method:
DBA(t) / (DBA(t) + CBA(t))
where DBA is assets of deposit money banks (lines 22a-d) and CBA is central bank assets (lines 12 a-
d).
9. Data on Private Credit are calculated using IFS numbers, using the following method:
((0. 5)*[F(t)/P e(t) + F(t- 1)/P_e(t- 1)] }/[GDP(t)/P_a(t)]
where F is credit by deposit money banks and other financial institutions to the private sector (lines
22d + 42d), GDP is line 99b, P_e is end-of period CPI (line 64) and P a is the average CP] for the
year.
Table 1: Summary Statistics: 1960-1995
Financial Intermediary Development
Liquid Commercial- Private
Liabilities Central Bank Credit
Mean 43.44 78.16 38.29
Median 37.48 83.89 27.01
Maximum 143.43 98.99 141.30
Minimum 9.73 23.72 4.08
Std. Dev. 25.61 18.26 28.71
Observations 71 71 71
LIQUID LIABILITIES = liquid liabilities of the financial system (currency plus demand
and interest-bearing liabilities of banks and nonbank financial intermediaries) divided by GDP, times 100.
COMMERCIAL-CENTRAL BANK = assets of deposti money banks divided by assets of deposit money banks
plus central bank assets, times 100.
PRIVATE CREDIT = credits by deposit money banks and other financial instittutions
to the private sector divided by GDP, times 100.
Table 2: Legal Origin and Financial Intermediary Development
Financial Intermediary Development
Liquid Liabilities Commercial-Central EPrivate Credit
C 3.829 0.958 4.506 3.063 4.027 -0.674
(0.000) (0.081) (0.000) (0.000) (0.000) (0.386)
ENGLISH -0.134 0.249 -0.170 0.022 -0.717 -0.090
(0.325) (0.038) (0.002) (0.716) (0.002) (0.646)
FRENCH -0.434 -0.052 -0.270 -0.078 -0.894 -0.268
(0.001) (0.703) (0.000) (0.152) (0.000) (0.190)
GERMAN 0.477 0.683 0.048 0.152 0.401 0.738
(0.016) (0.000) (0.100) (0.010) (0.076) (0.002)
INCOME 0.330 0.166 0.541
(0.000) (0.000) (0.000)
Obs. 71 71 71 71 71 71
Prob(F-test) 0.001 0.000 0.040 0.000 0.000 0.000
R-square 0.23 0.44 0.12 0.30 0.26 0.55
LIQUID LIABILITIES = liquid liabilities of the financial system (currency plus demand
and interest-bearing liabilities of banks and nonbank financial intermediaries) divided by GDP, times 100.
COMMERCIAL-CENTRAL BANK = assets of deposti money banks divided by assets of deposit money banks plus central bank assets, times 100.
PRIVATE CREDIT = credits by deposit money banks and other financial institutions to the private sector divided by GDP, times 100.
Values for the financial intermediary development indicators are averages over the 1960-95 period.
ENGLISH = English legal origin
FRENCH = Napolenik legal origin
GERMAN = German legal origin
Scandinavian legal origin is the omitted category.
INCOME L Logarithm of real per capita GDP in 1960.
Table 3: Financial Intermediation and Growth: Cross-Section Regressions, 1960-95
Dependent variable: Real Per Capita GDP Growth, 1960-95
Instrumental variables: Legal Origin Dummy variables
Regression Set #1: simple conditioning information set
Explanatory coefficient standard t-statistic P-value Number of J- LM-test
Variable error Observations Statistic OIR
PRIVATE CREDIT 2.515 0.814 3.090 0.003 71 0.00189 0.13
COMMERCIAL-CENTRAL BANK 10.861 3.086 3.520 0.001 71 0.01626 1.15
LIQUID LIABILITIES 1.723 0.844 2.041 0.045 71 0.03491 2.48
Regression Set #2: policy conditioning information set
Explanatory coefficient standard t-statistic P-value Number of J- LM-test
Variable error Observations Statistic OIR
PRIVATE CREDIT 3.222 1.245 2.589 0.012 63 0.00799 0.50
COMMERCIAL-CENTRAL BANK 9.641 4.039 2.387 0.021 63 0.0373 2.35
LIQUID LIABILITIES 2.173 0.908 2.394 0.020 63 0.03799 2.39
Regression Set #3: full conditioning information set
Explanatory |coefficient standard t-statistic P-value | Number of 1- LM-test
Variabie error Observations Statistic OIR
PRIVATE CREDIT 3.356 1.150 2.918 0.005 63 0.02239 1.41
COMMERCIAL-CENTRAL BANK 11.289 3.258 3.465 0.001 63 0.00325 0.20
LIQUID LIABILITIES 2.788 0.903 3.089 0.003 63 0.03901 2.46
Critical values for LM-Test Qver identifying Restrictions (2 d8f.): 10% 4.61; 5%=5.99
Simple conditioning information set: logarithm of Initial income per capita and schooling
Policy conditioning Information set: simple set, plus government size, inflation, black market premium, and openness to trade.
Full conditioning information set: policy set, plus Indicatros of revolutions and coups, political assassinations, and ethnic diversity.
LIQUID LIABILITIES = liquid liabilities of the financial system (currency plus demand
and interest-bearing liabilities of banks and nonbank financial intermediaries) divided by GDP, times 100.
COMMERCIAL-CENTRAL BANK = assets of deposti money banks divided by assets of deposit money banks plus central bank assets, times 100.
PRIVATE CREDIT = credits by deposit money banks and other financial institutions to the private sector divided by GDP, times 100.
Table 4: Financial Intermediation and Growth: Dynamic Panel Regressions, Summary
Estimator conditioning information set LIQUID LIABILITIES COMMERCIAL - CENTRAL BANK PRIVATE CREDIT observations
System estimator simple 2.26 6.257 2.237 359
(0.001) (0.001) (0.001)
10.4631 [0.3291 10.283)
policy 2.713 2.807 1.448 359
(0.001) (0.001) (0.001)
[0.4161 10.567] (0.417]
First Differences simple 1.368 2.18 1.601 285
(0.036) (0.011) (0.001)
(0.424] [0.1411 [0.1971
policy 0.365 1.008 0.599 285
(0.467) (0.001) (0.001)
[0.279) 10.3581 10.3421
Levels simple 1.802 6.094 2.151 359
(0.015) (0.001) (0.001)
[0.2481 [0.210] [0.2591
policy 2.919 3.234 2.063 359
(0.001) (0.001) (0.001)
[0.344) (0.139) 10.3491
Numbers in parentheses are p-values for the coefficient and numbers in brackets are p-values for the Sargan-test
simple conditioning information set: logarithm of initial Income per capita, average years of secondary schooling
policy conditioning information set: simple set plus government size, openness to trade
inflation, black market premium
LIQUID LIABILITIES: liquid liabilities of the financial system (currency plus demand and
interest-bearing liabilities of banks and nonbank financial Intermedlalres) divided by GDP
COMMERCIAL - CENTRAL BANK: assets of deposit money banks divided by
assets of deposit money banks plus central bank assets
PRIVATE CREDIT: credit by deposit money banks and other financial indtitutions to
the private sector divided by GDP
Diff-Sargan tests
Difference Sargan Tests
Comparing the
System estimator critical value
with conditioning information set LIQUID LIABILITIES COMMERCIAL - CENTRAL BANK PRIVATE CREDIT 5% 10%
First Differences simple 15.195 9.942 13.944 16 26.3 23.54
policy (using I instrument per variable) 26.39 30.122 33.728 36 50.71 47.12
policy (using 2 instruments per variable) 11.318 13.668 13.43 36 50.71 47.12
Levels simple 47.362 50.639 53.129 50 67.22 63.07
policy 34.272 24.168 34.37 35 49.52 45.97
Table 5: Financial Intermediation and Growth:
Dynamic Panel Regressions, System Estimator
Regressors (1) (2) (3)
Constant 0.251 -7.648 2.637
(0.797) (0.001) (0r003k
Logarithm of initial income per capita -0.799 -0.303 -0.305
(0.001) (0.001) (0.006)
Govemment size' -0.999 -0.664 -1.614
(0.002) (0.014) (0.001)
Openness to trade' 0.388 0.667 0.634
(0.091) (0.001) (0.001)
Inflation2 1.179 -1.518 -0.41
(0.002) (0.001) (0.147)
Average years of secondary schooling 0.786 0.559 0.297
(0.001) (0.001) (0.001)
Black market premium2 -1.982 -1.096 -1.072
(0.001) (0.001) (0.001)
Liquid Liabilities' 2.713
(0.001)
Comm. vs. Central Bank' 2.807
(0.001)
Private Credit' 1.448
(0.001)
dummy 71-75 -0.984 -0.741 -0.923
(0.001) (0.001) (0.001)
dummy 76-80 -1.142 -0.805 -1.106
(0.001) (0.001) (0.001)
dummy 81-85 -3.379 -2.738 -3.107
(0.001) (0.001) (0.001)
dummy 86-90 -2.555 -1.904 -2.273
(0.001) (0.001) (0.001)
dummy 91-95 -3.513 -2.744 -3.054
_______________________________ (0.001) (0.001) (0.001)
Sargan test3 0.416 0.567 0.417
Serial correlaffon test4 0.607 0.957 0.797
p-values in parentheses
¶ In the regression, this variable is included as log(varable)
2 In the regression, this variable is included as log(1 + variable)
3 The null hypothesis is that the instruments used are not correlated with the residuals.
4 The null hypothesis is that the errors in the first-difference regression exhibit
no second-order serial correlation.
Table 6: Summary Statistics on the Legal and Accounting Environment
Legal and Accounting Environment
CREDITOR ENFORCE ACCOUNT
Mean -0.3 7.5 61.2
Median 0 8.2 64.0
Maximum 1 10.0 83.0
Minimum -2 3.6 24.0
Std. Dev. 1.1 2.0 13.5
Observations 44 44 40
CREDITOR index of secured creditor rights.
ENFORCE = index of law and contract enforcement.
ACCOUNT = index of the comprehensiveness and quality of company reports
Values for the legal environment indicators are averages over the 1982-95 period.
Values of accounting quality are assessments of company reports in 1990.
Table 7: Legal Environment and Financial Intermediary Development
Financial Intermediary Development
Liquid Commercial- Private
Liabilities Central Bank Credit
c 3.497 2.450 4.112 3.943 2.694 1.557
(0.000) (0.000) (0.000) (0.000) (0.002) (0.000)
CREDITOR 0.060 0.101 -0.008 -0.001 -0.034 0.011
(0.328) (0.034) (0.759) (0.947) (0.591) (0.852)
ENFORCE 0.217 0.167 0.041 0.033 0.236 0.181
(0.000) (0.000) (0.022) (0.014) (0.000) (0.000)
ACCOUNT 0.003 0.003 0.004 0.004 0.014 0.014
(0.536) (0.658) (0.108) (0.136) (0.009) (0.021)
LRGDPSH -0.181 -0.029 -0.197
(0.096) (0.454) (0.103)
Obs. 35 35 35 35 35 35
Prob(F-test) (0.000) (0.000) (0.005) (0.002) (0.000) (0.000)
R-square 0.625729 0.593971 0.381852 0.373959 0.634022 0.609374
LIQUID LIABILITIES = liquid liabilities of the financial system (currency plus demand
and interest-bearing liabilities of banks and nonbank financial intermediaries) divided by GDP, times 100.
COMMERCIAL-CENTRAL BANK - assets of deposti money banks divided by assets of deposit money banks plus central bank assets, times 100.
PRI.ATE CREDIT _ credits by deposl: money banks ard otear finandcia; institutions io the private secior divided by GDP, times 100.
CREDITOR index of secured creditor rights.
ENFORCE = index of law and contract enforcement.
ACCOUNT index of the comprehensiveness and quality of company reports
Table 8: Financial Intermediation and Growth: Cross-Section Regressions, 1960-95
Dependent variable: Real Per Capita GDP Growth, 1960-95
Instrumental variables: Legal Environment variables (CREDITOR, ENFORCE, & ACCOUNT)
Regression 1: simple conditioning information set
Explanatory coefficient standard t-statistic P-value Number of J- LMM-test
Variable error Observations Statistic OIR
PRIVATE CREDIT 1 2.097 0.298 7.032 0.000 35 0.080003 2.80
COMMERCIAL-CENTRAL BANK 12.734 4.686 2.718 0.011 35 0.073241 2.56
LIQUID LIABILITIES 2.935 0.499 5.887 0.000 35 0.040316 1.41
Regression #2: full conditioning information set
Explanatory coefficient standard t-statistic P-value Number of J- LM-test
Variable error Observations Statistic OIR
PRIVATE CREDIT 3.325 0.852 3.903 0.001 35 0.030649 2.33
COMMERCIAL-CENTRAL BANK 8.400 5.008 1.677 0.107 35 0.078816 3.31
LIQUID LIABILITIES 2.734 0.802 3.410 0.0021 35 0.099216 2.73
Critical values for LM-Test Over Identifying Restrictions ( 2 d.f. ): 10% 4.61; 5%=5.99
Simple conditioning information set: logarithm of initial income per capita and schooling
Full conditioning information set: simple set, plus government size, inftlation, black market premium, and openness to trade,
and indicatots of revolutions and coups, political assassinations, and ethnic diversity.
LIQUID LiABILITIES = liquid labilities of the financial system (currency plus dernand
and interest-bearing liabilities of banks and nonbank financial intermediaries) divided by GDP, tirnes 100.
COMMERCIAL-CENTRAL BANK = assets of depostl money banks divided by assets of deposit money banks plus central bank assets, times 100.
PRIVATE CREDIT credits by deposit money banks and other financial institutions to the private sector divided by GDP, times 100.
CREDITOR = index of secuted credito rIghts.
ENFORCE = Index of law and contfact enfotcement.
ACCOUNT index of the comprehensiveness and quality of company repors
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